Python labelEncoder 在 sklearn 中的工作
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Working of labelEncoder in sklearn
提问by Neo
Say I have the following input feature:
假设我有以下输入功能：
hotel_id = [1, 2, 3, 2, 3]
This is a categorical feature with numeric values. If I give it to the model as it is, the model will treat it as continuous variable, ie., 2 > 1.
这是一个带有数值的分类特征。如果我将它按原样提供给模型，模型会将其视为连续变量，即 2 > 1。
If I apply sklearn.labelEncoder()then I will get:
如果我申请，sklearn.labelEncoder()那么我将获得：
hotel_id = [0, 1, 2, 1, 2]
So this encoded feature is considered as continuous or categorical? If it is treated as continuous then whats the use of labelEncoder().
那么这个编码特征被认为是连续的还是分类的？如果它被视为连续的，那么 labelEncoder() 的用途是什么。
P.S. I know about one hot encoding. But there are around 100 hotel_ids so dont want to use it. Thanks
PS我知道一种热编码。但是大约有 100 个 hotel_id，所以不想使用它。谢谢
回答by Tgsmith61591
The LabelEncoderis a way to encode class levels. In addition to the integer example you've included, consider the following example:
这LabelEncoder是一种对类级别进行编码的方法。除了您包含的整数示例之外，请考虑以下示例：
>>> from sklearn.preprocessing import LabelEncoder
>>> le = LabelEncoder()
>>>
>>> train = ["paris", "paris", "tokyo", "amsterdam"]
>>> test = ["tokyo", "tokyo", "paris"]
>>> le.fit(train).transform(test)
array([2, 2, 1]...)
What the LabelEncoderallows us to do, then, is to assign ordinal levels to categorical data. However, what you've noted is correct: namely, the [2, 2, 1]is treated as numeric data. This is a good candidate for using the OneHotEncoderfor dummy variables (which I know you said you were hoping not to use).
那么，LabelEncoder允许我们做的是为分类数据分配有序级别。但是，您注意到的是正确的：即，[2, 2, 1]被视为数字数据。这是使用OneHotEncoderfor 虚拟变量的一个很好的候选者（我知道你说你不希望使用它）。
Note that the LabelEncodermust be used prior to one-hot encoding, as the OneHotEncodercannot handle categorical data. Therefore, it is frequently used as pre-cursor to one-hot encoding.
请注意，LabelEncoder必须在单热编码之前使用，因为OneHotEncoder无法处理分类数据。因此，它经常被用作 one-hot 编码的前驱。
Alternatively, it can encode your target into a usable array. If, for instance, trainwere your target for classification, you would need a LabelEncoderto use it as your y variable.
或者，它可以将您的目标编码为可用数组。例如，如果train是您的分类目标，则需要 aLabelEncoder将其用作 y 变量。
回答by simon
If you are running a classification model then the labels are treated as classes and the order is ignored. You don't need to onehot.
如果您正在运行分类模型，则标签将被视为类，而顺序将被忽略。你不需要onehot。
回答by J. Doe
A way to handle this problem is to change your numbers to label with package inflect
处理此问题的一种方法是将您的数字更改为带有包装变形的标签
So I have been visiting all numbers of hotels id's and I have changed them into words for example 1 -> 'one' and 2 -> 'two' ... 99 -> 'ninety-nine'
因此，我一直在访问所有数量的酒店 ID，并将它们更改为单词，例如 1 -> 'one' 和 2 -> 'two' ... 99 -> '99'
import inflect
p = inflect.engine()
def toNominal(df,column):
for index, row in df.iterrows():
df.loc[index, column] = p.number_to_words(df.loc[index, column])
toNominal(df, 'hotel_id')
